The invention relates to a method for training a neural network with training data that characterize a financial market. Attempts are constantly being made to predict changes in a financial market, in order in this way to achieve an optimal asset allocation, also known as portfolio management. By an asset allocation is meant the investment of liquid capital in various trade options, such as for example, stocks, futures, bonds, or also foreign currencies, thus in all possibilities offered by a financial market as a whole. A portfolio is formed with the goal of achieving a maximum return for a predeterminable risk that the investor is willing to take within a predeterminable time interval.
From E. Elton et al., Modern Portfolio theory and Investment Analysis, John Wiley & Sons, Inc., 4.sup.th edition, New York, ISBN 0-471-53248-7, pages 15-93, 1981, basic principals of asset allocation and investment analysis are known. From this article, a model is likewise known, called the two-point model, for the specification of an achievable return dependent on a risk taken by the investor in the investment opportunity. However, the attempt to make predictions concerning changes in the financial market on the basis of this model is very imprecise, since neither the chronological aspect of the changes of the financial market nor the transaction costs that arise in trades on the financial market are taken into account.
A further disadvantage that can be seen in the model described in this article is that actual data concerning changes of the financial market can in no way be taken into account. This leads to an inflexible, imprecise statement concerning changes of the financial market, based on the model.
In addition, what is known as a reinforcement learning method is known for example from A. Barto et al., Learning to Act Using Real-Time Dynamic Programming, Department of Computer Science, University of Massachusetts, Amherst, Mass. 01003, pages 1-65, January 1993.